Post-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly recognised as imaging biomarkers of recovery and outcome, remain poorly captured by supervised segmentation methods. We evaluate REFLECT, a flow-based generative model, for unsupervised detection of both focal and non-lesional abnormalities in post-stroke patients. Using dual-expert central-slice annotations on ATLAS data, performance was assessed at the object level with Free-Response ROC analysis for anomaly maps. Two models were trained on lesion-free slices from stroke patients (ATLAS) and on healthy controls (IXI) to test the effect of training data. On ATLAS test subjects, the IXI-trained model achieved higher lesion segmentation (Dice = 0.37 vs 0.27) and improved sensitivity to non-lesional abnormalities (FROC = 0.62 vs 0.43). Training on fully healthy anatomy improves the modelling of normal variability, enabling broader and more reliable detection of structural abnormalities.

翻译：中风后磁共振成像不仅能够描绘局灶性病变，还能揭示继发性结构变化，如萎缩和脑室扩大。这些异常现象日益被视为康复与预后的影像学生物标志物，但现有监督分割方法对其捕捉能力仍显不足。本研究评估了基于流的生成模型REFLECT在中风后患者中检测局灶性与非病灶性异常的无监督性能。通过使用ATLAS数据集的双专家中心切片标注，采用自由响应ROC分析对异常图进行对象级性能评估。我们分别在无病灶的中风患者切片（ATLAS）和健康对照组（IXI）上训练了两个模型，以检验训练数据的影响。在ATLAS测试对象上，基于IXI训练的模型实现了更高的病灶分割性能（Dice系数=0.37 vs 0.27），并对非病灶异常表现出更优的敏感性（FROC=0.62 vs 0.43）。基于完全健康解剖结构的训练能更好地建模正常变异范围，从而实现对结构异常更广泛、更可靠的检测。